In this paper, the rock types of an iron ore deposit were classified using the digital image analysis technique. The image acquisition and analysis of blasted rocks were conducted in a laboratory for six different rock types. A total of 189 features were extracted from the individual rock samples using best-suited segmentation technique selected by validation study. The neural network technique was applied for rock classification model using image features. Five principal components, which accounts for 95% of total data variance, were selected as input parameters for the model. The misclassification error of the model for testing data was 2.4%.

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Name: Pete Schwalbach
Office: St Paul
Practice Area: OPA
Area of expertise: Environmental Regulatory Compliance
About Me in 140 Characters
Happily married, father of three, environmental regulatory consultant!
Favorite Thing about Being an OPA Practitioner
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